Research Data Management
What is RDM?
Research data management (RDM) refers to the management of research data throughout their entire life cycle. Important questions in this context include:
- How should newly created and processed data files, folders, and accompanying material be named, structured, and stored?
- How are they documented?
- How are the data sensibly prepared for possible reuse?
The so-called FAIR principles have evolved into de facto guiding principles for data management. The acronym FAIR stands for: Findable, Accessible, Interoperable, Reusable. Implementing these principles improves the reusability of data and makes data findable for new usage scenarios.
Why RDM?
Tending to greater openness and transparency in research, effective data management has become a fundamental part of the research process. It enables researchers to intuitively access, understand, and work with project data even years after leaving them. This saves a lot of time and resources and also helps to make research robust, replicable, and reproducible.
In addition, many funding agencies and journals now require the publication of data. Having a deliberate data management concept from the outset reduces the effort of publishing high-quality data markedly.
RDM at the Max Planck Society (MPG)
The MPG's Rules of Conduct for Good Scientific Practice contain clear guidelines on the security, storage, and documentation of research data (§2.4) as well as their accessibility (§2.7). The Max Planck Digital Library (MPDL) supports MPG researchers in all RDM-related matters with a central RDM team, more information can be found on the MPDL’s website on Research Data Management. In addition, the MPDL provides the Research Data Management Organizer (RDMO), a service that supports the organization of data management and the creation of data and software management plans, including templates for various funding organizations.
How is RDM supported at the MPIB?
A major part of research conducted at the MPIB is based on empirical, newly generated, often personal data, and encompasses diverse study designs (e.g., longitudinal, cross-sectional, experimental) and data collection methods (e.g., brain imaging, surveys). To accommodate the methodological and disciplinary diversity, the Institute has published its own Open Science and Research Data Management Guidelines, an institute-wide framework which defines the strategic commitment to accessibility and transparency ensuring that research is reproducible, reusable, and compliant with national and international standards (e.g., the FAIR principles or the UNESCO Recommendation on Open Science).
In addition, a dedicated RDM team supports researchers in the FAIR management of their research data. Via an internal tool developed specifically for the MPIB, we collect a small but relevant set of meta-information about studies. This enables the RDM team to offer tailored support throughout the entire data lifecycle: Among other things, this includes
- advice on data management plans,
- assistance with meaningful data organization and documentation,
- conversion of data into standardized formats such as BIDS for MRI data,
- sustainable use of storage space by allocating storage at the start of a study or releasing storage after the end of a study (in close cooperation with the central IT department), as well as
- advice on the selection of suitable licenses or repositories.
The management of personal data requires a high degree of attention, since processing these data must comply with the requirements of the European General Data Protection Regulation (GDPR). To support researchers in managing these particularly sensitive data and to ensure compliance with legal requirements, Castellum, another tool specifically designed for the MPIB, has been implemented.
Both tools allow the RDM team to support researchers in the GDPR-compliant management of their data during crucial phases of their studies, thereby improving the quality, accessibility, and reusability of research data at the MPIB.